Parental involvement is one of the primary factors that affect children’s development and individuality. However, its effect on the academic motivation and self-concept of college students is not well understood, especially in the Philippines. The present study used the Ecological systems theory by Bronfenbrenner to investigate the effect of varying parental involvement on the academic motivation and self-concept of college students at Dr. Carlos S. Lanting College. Purposive sampling was used among 198 college students from Dr. Carlos S. Lanting College (...) who were identified using a purposive sampling technique. Statistical instruments including descriptive, correlation, and regression analysis were used to examine the data collected and investigate the link between the variables. Based on the regression analysis, it was found that parental involvement only has a significant relationship with academic motivation and not with self-concept. In conclusion, college students have a sense of independence and their self-concept is not affected by parental involvement; nonetheless, parents providing essential support influence their academic motivation. -/- . (shrink)

This work demonstrates the implementation and use of an encoder-decoder model to perform a many-to-many mapping of video data to text captions. The many-to-many mapping occurs via an input temporal sequence of video frames to an output sequence of words to form a caption sentence. Data preprocessing, model construction, and model training are discussed. Caption correctness is evaluated using 2-gram BLEU scores across the different splits of the dataset. Specific examples of output captions were shown to demonstrate model generality (...) over the video temporal dimension. Predicted captions were shown to generalize over video action, even in instances where the video scene changed dramatically. Model architecture changes are discussed to improve sentence grammar and correctness. (shrink)

This work demonstrates the implementation and use of an encoder-decoder model to perform a many-to-many mapping of video data to text captions. The many-to-many mapping occurs via an input temporal sequence of video frames to an output sequence of words to form a caption sentence. Data preprocessing, model construction, and model training are discussed. Caption correctness is evaluated using 2-gram BLEU scores across the different splits of the dataset. Specific examples of output captions were shown to demonstrate model generality (...) over the video temporal dimension. Predicted captions were shown to generalize over video action, even in instances where the video scene changed dramatically. Model architecture changes are discussed to improve sentence grammar and correctness. (shrink)

This work demonstrates the implementation and use of an encoder-decoder model to perform a many-to-many mapping of video data to text captions. The many-to-many mapping occurs via an input temporal sequence of video frames to an output sequence of words to form a caption sentence. Data preprocessing, model construction, and model training are discussed. Caption correctness is evaluated using 2-gram BLEU scores across the different splits of the dataset. Specific examples of output captions were shown to demonstrate model generality (...) over the video temporal dimension. Predicted captions were shown to generalize over video action, even in instances where the video scene changed dramatically. Model architecture changes are discussed to improve sentence grammar and correctness. (shrink)